tbuckley/cabot-search huggingface dataset:
A meta-analysis of cardiac toxicity of concurrent epirubicin and trastuzumab in early breast cancer.
Authors: Minghan Yang, Chiun-Sheng Huang, Dwan-Ying Chang, I-Chun Chen, Ching-Hung Lin, Yen-Shen Lu
Journal: Journal of Clinical Oncology (2023)

e12583 Background: The concurrent use of doxorubicin and trastuzumab has been limited due to the increased risk of cardiac toxicity. Epirubicin is a less cardiotoxic alternative to doxorubicin, and the change in ejection fraction occurs until its cumulative dose doubles that with doxorubicin. This study aims to evaluate the cardiotoxicity and pathological complete response (pCR) rate associated with concurrent usage of epirubicin and trastuzumab in patients with HER2 positive early breast cancers (EBC). Methods: A literature search was conducted on NCBI/PubMed, and Cochrane database for phase II or III randomized controlled trials of concurrent use of epirubicin and trastuzumab in HER2 positive EBC between January 1, 2000 and February 28, 2021.The definition of adverse cardiac events varied among these trials, including changes in left ventricular systolic function and clinical heart failure. To compare the risk of cardiotoxicity and the odds of pCR rate between concurrent and non-concurrent regimens, a meta-regression analysis utilizing fixed-effects and mixed-effects linear models was conducted to investigate the relationship between various covariates. Results: Seven trials containing concurrent use of epirubicin and trastuzumab involving 1797 patients were analyzed. All of them are neoadjuvant studies. The median cumulative dose of epirubicin used was 300mg/m 2 and there were a total of 96 reported cardiac adverse events. The concurrent use of epirubicin and trastuzumab did not result in a statistically significant increase in cardiotoxicity compared to non-concurrent regimens (risk ratio = 1.18, 95% CI: 0.68-2.05). Meanwhile, there was a significant increase in pCR rate with the concurrent regimens (odds ratio = 1.48, 95% CI: 1.04-2.12). Mixed-effects meta-regression analysis showed that in trials with higher proportion of hormone receptor-positive EBC patients, the risk ratio of cardiotoxicity significantly increased with concurrent regimens and pathological complete response rate became less significant. Conclusions: The concurrent use of a limited dose of epirubicin and trastuzumab demonstrated a positive impact on pCR rate without a significant increase in cardiotoxicity. Further research is necessary to fully comprehend the long-term safety profile in individuals with metastatic breast cancer. [Table: see text]
The Tempus TIME trial portal and clinical trial enrollment.
Authors: Amol Rao, Kevin Miller, Chanh Trung Huynh, Rachel Luedtke, Mary Alice Anderson, Melissa Harris, Michael Johnstone, Lauren Murphy, Nicholas Morgan, Rachel Harmon, AnnaJane Ward, Blathnaid Donovan, Christie Rice, Scott Sherrin, Annie Darmofal, Danielle Skelly, Matthew M. Cooney
Journal: Journal of Clinical Oncology (2023)

1583 Background: The TIME Trial Program is a research network that screens &gt; 1 million patients daily via EMR integrations and NGS testing through an automated trial matching platform and human review. There are enormous complexities following patients longitudinally as their clinical status changes while accurately matching them with hundreds of studies. The TIME Trials Portal, which stores both clinical trial documents and patient status updates, allows for real-time communication between the TIME team and Clinical Sites. Methods: After both an automated and nursing review, eligible trial patients are uploaded into the TIME Trials Portal. This database tracks the patient’s name, date of birth, biomarkers, clinical status, trial matches, free text comments, and upcoming physician, radiology, and laboratory appointments. To protect Personal Health Information, each Clinical Site can only view their own patients. Patient status is classified as Needs Review, Monitoring, Enrolled, Inactive. Each update is time-stamped and tracked. Enrolled patients have the trial name, consent date, and other pertinent information documented. Results: The data from 2020 to 2022 includes 71 TIME Sites. Over this period, 6,816 patients were added to the TIME Trials Portal for an average of 219 per month. Of these, 1,891 patients were no longer eligible due to worsening performance status, trial closure, death, and other causes. The TIME team provided 8,643 patient updates compared to 7,442 Clinical Site updates. Clinical Sites spent 1,231.7 hours using the TIME Therapies Portal resulting in 623 total consents. Data from 2022 alone, demonstrated that 30 (42.3%) Clinical Sites with EMR integrations were responsible for 4,491 (76.3% of the total) patient updates translating into 288 (82.1%) of all consents. Also in 2022, of the 27 (38%) Clinical Sites that updated their patient’s status &gt; 50 times consented 291 (82.9% of the total) patients. Conclusions: By 2022, the TIME Trials Portal was averaging approximately 400 new patients monthly resulting in a 13.1X increase in patients added from the program’s inception. Clinical Sites updated the patient status over 7,400 times spending over 1,200 hours using the TIME Trials Portal. The highest-performing Clinical Sites, which enrolled approximately 80% of the total patients, included those with EMR integrations and/or &gt; 50 patient updates yearly. The TIME Trials Portal enabled patient tracking and communication to facilitate clinical trial enrollment.[Table: see text]
Gene expression profiling of paired primary breast cancers and brain metastases to identify brain metastases-specific biological changes.
Authors: Gaia Griguolo, Maria Vittoria Dieci, Susan Fineberg, Claudia Pinato, Michele Bottosso, Luc Bauchet, Federica Miglietta, Jack Jacob, Giovanni Zarrilli, Valerie Rigau, Maria Cristina Guarascio, Francesca Zanconato, Francesca Schiavi, Matteo Fassan, William Jacot, Stefano Piccolo, Amelie Darlix, Valentina Guarneri
Journal: Journal of Clinical Oncology (2023)

1032 Background: Despite the clinical impact of breast cancer (BC) brain metastases (BM), their biological complexity still remains poorly understood. We here evaluate the genomic profile of paired primary and metastatic samples to characterize biological changes acquired by BC during metastatization to the brain. Methods: The expression of 758 BC–related genes was evaluated using the BC360 Panel (nCounter platform) in matched primary BC and their associated BM. Intrinsic molecular subtyping was determined using the PAM50 subtype predictor (Parker et al. JCO 2009). Hormone receptor (HR) and HER2 status were evaluated on the BCBM. A False Discovery Rate (FDR) corrected paired two-class SAM was used to identify changes in single genes expression between paired BC and BMs samples. Results: Twenty-one matched primary BC and BMs samples were analyzed. BC subtype evaluated on the BMs was distributed as follows: 24% (N=5) HR-/HER2-, 24% (N=5) HR+/HER2- and 52% (N=11) HER2+. A considerable shift in PAM50 subtype was observed between primary BC and paired BM. While subtype concordance was high for HER2-enriched (HER2-E) tumors (100%), all Luminal A (LumA) primary BCs switched to either HER2-E (75%) or Luminal B (LumB, 25%) on the BM sample, as did 45% of Basal-like BCs (Basal to HER2-E). A clinical switch from HER2- primary BC to HER2+ BM (defined by IHC and ISH) was observed in only one pair (5%). Consistently, HER2-E (p&lt;0.001) and LumB (p=0.001) PAM50 signatures were significantly more expressed, while the LumA (p&lt;0.001) and Normal-like (p=0.001) signatures were significantly less expressed in BMs as compared to paired primary BCs (FDR-corrected Wilcoxon paired signed rank test). No significant change was observed in the expression of the Basal-like signature (p=0.562). Among the 758 evaluated genes, 60 and 318 genes, respectively, were significantly up- and downregulated in BMs as compared to primary BC (FDR&lt;5%). Upregulated genes were enriched, among others, in genes involved in survival and migration (e.g. FGFR4), cell proliferation (e.g. CCNB1, CDK1) and HER2-amplicon (e.g. ERBB2, GRB7). Downregulated genes were enriched in genes involved in immune response (e.g. CD8A, CCL5, PDCD1LG2, TNF), angiogenesis (e.g. PDGFRB) and response to hormonal stimuli (e.g. ESR1, BCL2). Conclusions: Gene-expression profiling of matched primary BCs and BMs shows recurrent gene expression modifications in metastatic samples which might have potential therapeutic implications (e.g. acquisition of HER2-E subtyping and increase in ERBB2 mRNA levels). [Table: see text]
Can synthetic data accurately mimic oncology clinical trials?
Authors: Samer El Kababji, Nicholas Mitsakakis, Xi Fang, Ana-Alicia Beltran-Bless, Gregory Russell Pond, Lisa Vandermeer, Dhenuka Radhakrishnan, Lucy Mosquera, Mark J. Clemons, Khaled El Emam
Journal: Journal of Clinical Oncology (2023)

1554 Background: There is strong interest by researchers, the pharmaceutical industry, medical journal editors, funders of research, and regulators in sharing clinical trial data. Reusing data extracts the most utility possible from patient contributions. The majority of patients do want to share their data for secondary research purposes. However, data access for secondary analysis remains a challenge. A key reason why individual-level data is not made directly available to data users by authors and data custodians is concern over breaches of patient privacy. Synthetic data generation (SDG) is an effective way to address privacy concerns that can enable the broader sharing of clinical trial datasets. However, a key question is whether the reproducibility of the generated data is adequate to draw reliable conclusions. Methods: We synthesized datasets from five pragmatic breast cancer clinical trials performed by the REaCT group (https://react.ohri.ca/). A sequential synthesis method, a type of machine learning was performed. The published analysis of each trial was repeated on each synthetic dataset to evaluate reproducibility. We evaluated reproducibility on three criteria: (a) decision agreement: the direction and statistical significance of the primary endpoint effect estimates are the same as the real data, (b) estimate agreement: the parameter estimates from the synthetic data are within the 95% confidence interval of the real data, and (c) the confidence interval overlap between real and synthetic parameters is above 50%. In addition, we evaluated privacy using a membership disclosure metric. This evaluates the ability of an adversary to determine that a target individual was in the original dataset using the synthetic data, computed as an F1 classification accuracy score. Results: Our results show that decision and estimate agreements held true across all five trials, and the confidence interval overlap was high. The risks of membership disclosure are all below the established 0.2 threshold. Conclusions: In this study, we were able to successfully generate synthetic datasets that accurately replicated original data from 5 oncology trials and yielded the same results as in the original published studies, with a very low risk of membership disclosure. With proper modeling techniques, synthetic datasets can play a key role in data democratization and the reuse of oncology clinical trials.[Table: see text]
Characteristics and predictive modeling of HER2 mutation in patients with non-small cell lung cancer.
Authors: Uras Varolgüneş, Garima Mittal, Kaushal Parikh, Andrew Ip, Jyoti Malhotra, Benjamin Bates, Maribel Salas, Meena Arunachalam, Haoqian Chen, Wenjin Chen, Dantong Yu, Soko Setoguchi
Journal: Journal of Clinical Oncology (2023)

e13559 Background: Recent advances in targeted therapies have increased the survival and quality of life for non-small cell lung cancer (NSCLC) patients. Understanding clinical characteristics and predicting HER2 status may facilitate efficient use of next-generation sequencing (NGS) testing and promote precision medicine/targeted therapies. Methods: NSCLC patients ≤65 years old, diagnosed between 01/2011 and 06/2021 who have NGS test results were identified in from two National Cancer Institute designated cancer centers in the Northeastern region of the United States. Demographics, behavioral, and clinical characteristics were extracted from electronic health record (EHR) data and EHR-linked cancer registry. Prevalence prediction modelling of HER2/ERBB2 mutation was performed using machine learning (ML) methods including logistic regression (LR), random forest (RF), naïve Bayes (NB), extreme gradient boosting (XGBoost) and multi-layer perceptron (MLP). 10- fold cross validation and k-nearest neighbor method were used to impute missing values. Under-sampling, oversampling and feature selection methods were tried. Results: Among 761 NSCLC patients with NGS reports available, 353 (46%) were ≤65 years old. Among 353 (mean age 57, 58% female, 88% non-Hispanic, 69% white, 57% had metastasis, 58% prescribed chemotherapy) 25 (7%) had a HER2 mutation. Based on univariate significance tests with LR on the complete data: cerebrovascular disease, BMI between 27.5-29.9, KRAS, EGFR and MET were significant with odds ratios (ORs) ranging from 0.2-3.9. In our prediction models, BMI, cerebrovascular disease, weight loss, hepatic metastases, diabetes, congestive heart failure, and KRAS, EGFR, MET and RET alteration status were significant predictors in multivariate analyses, suggesting the potential usefulness of these features in predicting HER2 mutation among patients with NSCLC. Area under the ROC curve (AUC) varied across different models from 70.9% (RF), 70% (XGB and MLP), 69.5% (LR) to 68.2% (Naïve Bayes). The best performing model with RF yielding an AUC ~71% can provide relative high specificity and acceptable sensitivity depending on the cut off. Conclusions: Our prediction models yielded AUC around 70% with the best performing model’s AUC being 71% in a subgroup aged ≤65. After further validation, our prediction model may be used to identify patients appropriate for repeat NGS testing who did not have HER2 testing in first molecular profiling. Future work is needed to predict status in repeat testing with previously test negative patients who relapse or have progression of disease. Random forest (RF) Model performance across different cutoff for the predicted probability. [Table: see text]
Use of machine learning to predict pathological complete response using multi-modal data from I-SPY2 clinical trials.
Authors: Fahad Shabbir Ahmed
Journal: Journal of Clinical Oncology (2023)

592 Background: The objective of the current analysis of the I-SPY 2 data from multiple breast cancer clinical trials for stages II and III cancers was to look at the possibility of developing a machine learning algorithm to predict pathological complete response using multimodal data. Methods: Imagining, clinical and biomarkers data form the I-SPY 2 trial was used in this analysis. Four different experiments were designed for to assess different data pre-processing and machine learning methods mainly deep neural networks (DNN) and random forest (RF) classifiers. Experiment 1 and 3 use DNNs while Experiment 2 and 4 used RFs; more over the first two experiments used data that was continuous in nature from the point of view of pre-performed imaging analysis while in later two experiments we had binarized all the variables using a median cutoff for high vs low values. The variables used were treatment recieved, HER2/neu receptor status, hormone receptor status, age, ethnicity, gynecological history, MRI feature data. All models were evaluated using robust testing methods that included sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV), accuracies (training and test sets), area-under the receiver-operator curve and F1-Score. Results: From a total of 985 patients with clinical data we used 384 that had multi-feature MRI data. Time series data for the initial scan (T0) and a scan 3 weeks later (T1). The final comparative analysis showed the best model among the 4 experiment the best performing algorithms for this analysis was random forests using median cutoffs with a best F1-score (0.43), specificity (86.4%), PPV (47.8%), accuracies (train sets, 100%; test sets, 75%), and AUROCs (0.78, 0.56-0.73). Conclusions: The use of machine learning to predict pathological complete response using multimodal data shows good potential as a digital biomarker. However, these results need further validation before a clinical tool is available for the clinicians. [Table: see text]
Correlation analysis to assess surrogate endpoints for overall survival (OS) in triple-negative breast cancer (TNBC).
Authors: Judith Pérez-Granado, Ankit Kalucha, Kinisha Gala, Olga Bodriagova, Laura Vidal, Bruno Larvol, Mark Gramling, Kamal S. Saini
Journal: Journal of Clinical Oncology (2023)

e13089 Background: The use of surrogate endpoints can help reduce the trial duration and cost which is important for aggressive diseases such as TNBC. Previous studies suggest progression-free survival (PFS) may serve as a surrogate for OS in metastatic TNBC. We aim to evaluate PFS and additional surrogate endpoints for their correlation with OS in localized and advanced TNBC. Methods: TNBC phase 3 trials and pooled analysis as well as trials including TNBC subgroups, were identified through a systematic search using LARVOL CLIN, PubMed, clinicaltrials.gov, and conference proceedings. Out of these, trials reporting median survival and hazard ratios (HR) for both OS and surrogate endpoints were considered for further statistical analysis. Surrogate endpoints reported by fewer than 4 trials were excluded from the study. Pearson correlation (r) and determination coefficient (R 2 ) were calculated to assess surrogacy between Median OS and Median PFS, HR OS and HR PFS, HR disease-free survival (DFS), and invasive disease-free survival (IDFS). Weighted regression analysis was conducted controlling for sample size among trial arms. Results: 26 TNBC cohorts and 2 pooled analyses evaluating PARP, PD-1, PD-L1 inhibitors, and chemotherapies were included. The association analysis results are shown. There was a significant correlation between median OS and median PFS (r = 0.73, p value = 1.026×10 −6 ) as well as between HR OS and HR PFS (r = 0.61, p value = 0.009). There was also a significant correlation between HR DFS and HR IDFS with HR OS (r = 0.92, p value = 0.003; r = 0.99, p value = 0.014). Conclusions: PFS is significantly correlated with OS in TNBC across diverse therapies. Also, HR PFS, HR DFS, and HR IDFS are significantly correlated with HR OS. Overall, these results illustrate that PFS could be used as a surrogate for OS in TNBC. Subsequent studies should include early-phase trials to increase association robustness and enable correlation comparison between the diverse mechanism of actions and other surrogate endpoints. [Table: see text]
Correlation between Oncotype DX, Predict, and Nottingham Prognostic Index on HR+, HER2- early breast cancer treatment: The current value of molecular testing.
Authors: Leticia Campoverde, Felipe Camacho, Adriana Sanchez, Paulina Lugo
Journal: Journal of Clinical Oncology (2023)

1601 Background: The Oncotype DX Breast Recurrence Score (ODX) assay is a 21-gene assay validated in hormone receptor-positive, HER2 negative invasive breast cancer (BC) to predict chemotherapy benefit and risk of distant recurrence at 10 years regardless of node status and is also a strong predictor of mortality. ODX can minimize the misuse of chemotherapy, financial burden, and improve clinical outcomes. ODX is not widely used in Ecuador due to socioeconomic and healthcare access disparities; instead, many physicians stratify patients' risk according to clinicopathological and immunohistochemical features. We aim to compare the differences between ODX and two of the most common stratification clinical tools: Predict Score (PS) and Nottingham prognostic index (NPI). Methods: Retrospective analysis of women with early BC who underwent ODX testing in Ecuador from May 2020 to January 2023. Inclusion criteria: early BC (T1-T3 / N0-N1a); estrogen receptor (ER) positive; HER2 negative by immunohistochemistry; and available data of tumor size, grade, lymph node status, and Ki67 proliferation index. Exclusion criteria: patients with recurrent BC, incomplete pathology, or immunohistochemistry data. PS and NPI were calculated using online tools. The correlation between PS, NPI, and ODX scores was analyzed using Spearman's correlation coefficient in SPSS. Results: 77 patients underwent ODX testing, of which 58 patients (mean age 55 years old) met the inclusion criteria. Invasive breast carcinoma not otherwise specified was the most common type (48 patients (82%). Risk scores generated by PS correlated strongly with NPI for all patients (r=0.47; p ≤ 0.001). A strong correlation was also found between ODX and NPI (0.79, p&lt;0.027). Risk scores were then stratified by patient risk category. For low-risk patients, 68% of correspondence between ODX and PS was found, and 98% between ODX and NPI. Intermediate-risk patients showed a correspondence between ODX and PS of 5% and 9% for ODX and NPI. High-risk patients showed a correspondence of 50% between ODX and PS and 17% between ODX and NPI. Conclusions: ODX risk recurrence for low-risk patients is highly correlated with PS and NPI. For Intermediate and high-risk patients determined by PS and NPI, ODX testing provided valuable and prognostic information and should be considered to guide adjuvant treatment. [Table: see text]